Self-Learning Thermoblock Heating for Faster Coffee Machine Start-Up
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Thermoblocks used in beverage preparation machines face challenges in accurately controlling temperature and optimizing heating energy, leading to lengthy pre-heating periods and variability in heating performance due to thermal inertia and environmental factors.
Innovation Solution
A self-learning heating device with a thermoblock and controller that adjusts preheating duration and power intensity based on monitored temperature differences, learning from past start-ups to optimize heating time and adapt to changing conditions, reducing preheating time by up to 70%.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a thermoblock with high thermal capacity metal mass is used to accumulate heat energy, then the ability to transfer heat to circulating liquid is improved, but the pre-heating time becomes excessively long
Solution Approach 1:
The system performs preliminary measurements of voltage, current, and temperature during startup to calculate expected heating parameters before actual heating begins. This allows the controller to pre-determine optimal heating duration and power levels, reducing actual pre-heating time while maintaining effective heat transfer.
Solution Approach 2:
The system continuously monitors voltage, current, and temperature during operation and uses this feedback to adjust heating parameters in real-time. This closed-loop control optimizes the balance between heat transfer efficiency and heating time by adapting to actual system conditions rather than relying on fixed parameters.
2Measurement precision
If dynamic loop-controlled powering with continuous temperature measurement is implemented, then temperature control accuracy is improved, but the system complexity increases
Solution Approach 1:
The system replaces complex continuous analog temperature control mechanisms with a digital calculation-based approach. By measuring voltage, current, and temperature and calculating expected temperature evolution, the system achieves accurate temperature control through computational methods rather than complex mechanical or analog control systems.
Solution Approach 2:
The system changes control parameters dynamically based on measured conditions. Instead of maintaining a fixed complex control architecture, the system adjusts heating duration, power levels, and control thresholds based on real-time measurements of voltage, current, and temperature, simplifying the control system while maintaining accuracy.
3Adaptability or versatility
If the thermoblock adapts to different environmental conditions and system variations, then heating performance consistency is improved, but the control algorithm complexity increases
Solution Approach 1:
The system performs self-characterization during startup by automatically measuring its own voltage, current, and temperature parameters and calculating its specific heating characteristics. This self-service approach allows the thermoblock to adapt to environmental conditions and manufacturing variations without requiring external calibration or complex pre-programmed adaptation algorithms.
Solution Approach 2:
The system performs preliminary measurements and calculations during startup to establish adaptation parameters before normal operation begins. By pre-calculating voltage drops, heating power, and temperature coefficients during the startup phase, the system prepares adaptation data in advance, reducing the complexity of real-time control algorithms during subsequent operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The self-learning system efficiently heats the beverage preparation machine to the desired temperature in the shortest possible time, minimizing the need for fine-tuning and ensuring consistent performance across different environments and conditions.
Implementation Method 1
They generally comprise a heating chamber, such as one or more ducts, in particular made of steel, extending through a mass of metal, in particular a massive mass of metal, in particular made of aluminium, iron and/or another metal or an alloy, that has a high thermal capacity for accumulating heat energy
Implementation Method 2
a high thermal conductivity for the transfer the required amount of the accumulated heat to liquid circulating therethrough whenever needed
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention concerns a unit (1000) for controlling transmission of power to a thermal conditioning device (100) e.g. for coffee machine, comprising a controller (2) with a start-up profile for starting-up said device (100) from a temperature of inactivity (TI) to an operative temperature for bringing to a target temperature (TT) a fluid circulating through said device (100) at start-up end, said controller (2) being arranged to allow circulation of fluid through said device (100) at start-up end and to compare the determined temperature (SOT) of fluid circulated at start-up end to the target temperature (TT) and derive a temperature difference therefrom. It is characterized in that the start-up profile has at least one parameter and in that said controller (2) has a self-learning mode for adjusting said parameter as a function of said temperature difference and to store the adjusted parameter for a subsequent starting-up of said device (100). The invention concerns in particular a method for optimized heating up of a coffee machine (104).